1. Technical Field
The present invention relates to analysis of data records, and, more particularly, to methods, systems and devices that conduct stability analysis on complex data sets.
2. Description of the Related Art
Many supply chain applications exhibit significant run to run variations that are, at least in part, a function of shifts in data, rules and parameters. While data is a key driver, individual data sets tend to be both large and complex, thereby rendering query based analysis of run to run shifts in data sets tedious, difficult, time consuming, inconsistent and error prone.
Query-based net change analysis of data sets operates on limited comparisons between a horizon current data set and a prior data set. Because queries are limited to table-specific standard Structured Query Language (SQL) queries, rule-based directives are out of the scope of these types of analyses. The query-based net change result consists of a list of item quantity pairs with indications that differences occur or do not occur between elements of a given pair. Balancing performance with the capability of analyzing large data sets is problematic. For example, complex queries run against large data sets can easily consume hours of monitoring time by a user and can consume significant system resources. For very large data sets, queries can often fail to produce a result.
Beyond straight SQL-based queries, stored procedures or Java Database Connectivity (JDBC)/Open Database Connectivity (ODBC)-based applications can be used to extend analysis capability and thereby provide sophisticated diagnostics. However, use of such objects involves custom programming, which is seldom reusable across different data sets. As with straight SQL-based analysis methods, resource and performance issues arise with the application of extended capabilities to very large data sets, or to sets of very large data sets.